Commonly, professors, students and researchers from universities around the world use software distributed under a license agreement for computer simulation purposes, which requires a computer with considerable hardware capabilities. Consequently, this implies a high cost to conduct simulations that require implementing numerical methods in all areas of engineering, particularly in the field of robotics and nonlinear control. This paper presents the design and results analysis of a low-cost and open-source frugal computer simulation tool with applications to robotic nonlinear control, for instance, for numerical simulations of manipulator robot control based on dynamic models. Using a single board minicomputer with with reduced computing power, Raspberry Pi, together with free software, GNU-Octave, trajectory tracking control simulations in the joint space of a Selective Conformal Assembly Robot Arm (SCARA) are achieved by solving a system of nonlinear differential equations, represented in matrix form, which includes the control law and the system model. The results of the proposed alternative are compared by running the same simulation code on a laptop computer using MATLAB and GNU Octave, which show minimal deviations and reasonable time complexity. Moreover, considering the frugality curve calculated for this approach, in addition to the low acquisition cost of the simulation software tool, it would allow the creation of a simulation laboratory in some universities with budgetary constraints for educational and research purposes.

Estimating peak flow for a catchment is commonly undertaken using the design event method, however this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of stream flow at the outlet of the catchment. With this data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a one in 100 year annual exceedance probability peak flow, the peaks over threshold method (333m3/s ±50m3/s) was significantly less uncertain than the annual maximum method (427m3/s ±100m3/s). For the one in 100 year annual exceedance probability, the median peak flow from the peaks over threshold method (333m3/s) produced an outcome comparable to the design event method peak flow (328m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, the paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.

Acoustic emission (AE) is caused by the sudden release of energy by a material as a result of material degradation related to deformation, crack, or fault within a solid. The same situation also occurs in leaks caused by turbulence in the fluid around the leak. In this study, analytical modeling for AE due to leakage through a circular pinhole in a gas storage cylinder was performed. The displacement fields responsible for AE, excited by the concentrated force (CF) associated with the turbulent flow though the pinhole, were derived by solving the Navier-Lamé equation. The CF as an excitation source was formulated in terms of a fluctuating Reynolds stress (FRS) and spatial Green’s function. In particular, a series of experiments were conducted under different operating conditions to explore the characteristics of the AE signals due to leak in a gas cylinder. Finally, the simulation and experimental results were compared to verify the accuracy of the simulation results.

For deep-penetrating geochemistry, there is a frontier issue as to how to explore ore-bearing information deep in covered areas, while it is, too, important to investigate the geochemical migration of ore-forming elements for the development of exploration geochemistry. In this study, a 380-day migration column experiment was conducted with surface soil and pure Cu powder collected from the Yujiashan study area in Wuhan City, Hubei Province taken as the object of research to simulate the migration process of ore-forming elements under natural conditions. The results showed obvious vertical migration of the ore-forming elements and conversion of mobile metal forms in the process; further, according to 6 groups of simulation experiments that took 45 days to conduct on migration column samples and the surface oil and Cu powder collected from the Pulang mining area in Yunnan Province, the vertical migration of the ore-forming element was still visible, and the exogenous substance (pure Cu powder) could enhance the anomaly without changing the basic migration mechanism. This indicates that Cu might migrate in a stable manner in soil cover in the Pulang mining area, and its migration was mostly driven by the transformation of Fe-Mn oxide bound state and organic bound state; finally, a multifractal analysis was conducted, revealing that the ore-forming elements presented a nonlinear and complex structure during their upward migration, which caused anomalies in the covered area, and multiple parameters exerted a certain indicative effect on different source intensities and properties of soil. In this study, laboratory simulations were performed to summarize the active-state evolution pattern and migration mechanism of ore-forming elements with a view to providing technical support and theoretical guidance for geochemical prospecting in the covered region.

To solve the problem of deformation and stress in segments when a full-face hard rock tunnel boring machine (TBM) tunnel for an urban subway passes through existing buildings, a three-dimensional mechanical calculation model of TBM tunneling under existing buildings was established based on numerical analysis to comprehensively study the spatial attributes of tunnel segments during TBM tunneling. The Qingdao Metro Line 1 TBM tunnel in the hard rock stratum of Xiaobei District passes through existing buildings. The results indicated that (1) the stress and deformation of the segments were different at different positions. The horizontal and vertical stress and displacement of the segments in front of the building were significantly affected by the building. When the segment was not in the area below the horizontal plane of the building, the vertical stress of the segment was significantly greater than the two stresses in the horizontal direction. (2) The vertical displacements of the vault and the arch bottom were the largest, and the influence of vertical accessories was stronger when the segment was closer to the building.(3) When the tunnel was excavated to approximately one tunnel diameter in front of the building, the building was affected. If the surrounding rock is unstable, the excavation face may collapse and the ground surface may sink, possibly leading to inclination, cracking, and uneven settlement of the building.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

High-fidelity patient simulation (HFPS) is widely used in professional training to enhance students’ competence in clinical management. Problem-solving (PS) and clinical reasoning (CR) skills are essential to developing students in professional competence in safe and effective care. These two skills should be initiated at the early training. However, little is known about the effects of HFPS on developing PS and CR skills in first year undergraduate students. Therefore, this pre- and post-experimental study aimed to investigate the effects of HFPS on development of PS and CR skills in first-year nursing students. The students were required to go through four sessions, preparation, pre-briefing and orientation, simulation role-playing, and debriefing, for the HFPS. The study utilized the Problem-Solving Inventory (PSI) and the Nurses Clinical Reasoning Scale (NCRS) to measure problem-solving and clinical reasoning abilities before and after HFPS. Bivariate analysis, one-sample t-test, and independent t-test were performed to evaluate the performance of the PS and CR skills at the two study periods. One hundred eighty-nine students were recruited, with a mean age of 20.56 years, and 73% were female students. The analytic results showed that the PSI, particularly in domains of Problem-Solving Confidence (PSC) (p<0.001) and overall PS (p<0.001), and the CR (p<0.001) had significant improvement after HFPS. The study concluded that HFPS is an effective innovative method to significantly improve students’ problem-solving and clinical reasoning abilities. Nurse educators play an important role in providing explicit learning instructions in a simulation guideline that directs and guides students to learn at each stage of HFPS. Students should take more initiative and engage in their learning through HFPS to enhance their competence in knowledge and higher intellectual skills for personal and professional developments.

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Ditching-rotary tiller was widely used in tillage and ditching furrow during rapeseed sowing. As its ditching furrow system (consist of four identical ditching plows) would bear great drag resistance from the soil during operation, the ditching plow was optimized based on differential geometry analysis and EDEM simulation. Resistance-velocity simulation indicated the plow with parabolic type guide curve had better reduce-drag characteristics. Differential geometry analysis indicated decay rate of E and growth rate of G could represent the variable regularity of drag resistance with the increase of velocity. The L value could represent the magnitude of drag resistance. The smaller L value, decay rate of E and growth rate of G, the better drag-reduce characteristics. Quadratic rotation orthogonal combination design shown the optimal structural parameter values were: Δθ=15°, θmin=45°, h=250mm, l=100mm. The minimum average drag resistance was 230.1N. The differential geometry analysis of optimal ditching plow shown |dE/dκ| and |dG/dκ| could obtain minimum value, indicating growth rate of drag resistance for optimal plow was the lowest within the experimental level. Field experiment indicated the operation effect of optimized ditching furrow system could met the requirements of rapeseed sowing. The research provided in-depth theoretical guidance for Drag-reduce design of plow.

The study of energy sources has been an awareness of the modern world due to constraints on the current energy worldwide supply chain. The complexity of the theme as well as the consciousness of finite sources also the need of saving energy has become a priority. Although many papers have moved in this direction there is no record of papers studying the variables in a house heating system, which represents the highest energy consumption in many cold countries. This paper explores the energy-saving theme by studying the main variables present in houses’ heating systems in the winter season in cold countries like Canada, Russia, Norway, Iceland, Finland, etc. By using a novel combination of numerical thermal simulation by COMSOLTM and statistical analysis by MiniTabTM , it was possible to design an efficient thermal distribution in a house with the variables of the system by determining their significance and interaction qualitatively.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

With the popularization of natural gas and the requirements for environmental protection, the development and utilization of natural gas is particularly important. The status of natural gas in China's oil and gas exploration and development is constantly improving, and the country is paying more and more attention to the exploitation and utilization of natural gas. The Upper Paleozoic tight sandstone in the Ordos Basin is characterized by low porosity, low permeability and large area of concealed gas reservoirs. By injecting CO2 into the formation, the recovery of natural gas can be improved, and at the same time, the stable storage of CO2 can be achieved to achieve a win-win situation of CO2 emission reduction and utilization. Injecting greenhouse gas CO2 into gas reservoirs for storage and improving recovery has also become a hot research issue. In order to improve the recovery efficiency of tight sandstone gas reservoir, this paper takes the complex tight sandstone of Upper Paleozoic in Ordos Basin as the research object, through indoor physical simulation experiments, carried out the influence of displacement rate, fracture dip angle, core permeability, core dryness and wetness on CO2 gas displacement efficiency and storage efficiency, and analyzed the influence of different factors on CO2 gas displacement efficiency and storage efficiency to improve the recovery and storage efficiency. The research results show that under different conditions, when the CO2 injection pore volume is less than 1PV, the relationship between the CH4 recovery rate and the CO2 injection pore volume is linear, and the tilt angle is 45 °. When the CO2 injection pore volume exceeds 1PV, the CH4 recovery rate increases slightly with the increase of displacement speed, the recovery rate of CO2 displacement CH4 is between 87% - 97%, and the CO2 breakthrough time is 0.7PV-0.9PV. In low-permeability and low-speed displacement cores, the diffusion of CO2 molecules is more significant. The lower the displacement speed is, the earlier the breakthrough time of CO2 is, and the final recovery of CH4 slightly decreases. Gravity has a great impact on CO2 storage and enhanced recovery. The breakthrough of high injection and low recovery of CO2 is earlier, and the recovery of CH4 is about 3.3% lower than that of low injection and high recovery. The bound water makes the displacement phase CO2 partially dissolved in the formation water, and the CO2 breakthrough lags about 0.1PV. Ultimately, CH4 recovery factor and CO2 storage rate are higher than those of dry core displacement. The research results provide theoretical data support for CO2 injection to improve recovery and storage efficiency in complex tight sandstone gas reservoirs.

The use of carbon nanotubes to improve the mechanical properties of polymers is one of the promising directions in materials science. The addition of single-walled carbon nanotubes (SWCNT) to a polymer results in significant improvement of mechanical, electrical, optical, and structural properties. However, the addition of SWCNTs does not always improve the polymer properties. Also, when a certain content of SWCNTs is exceeded, the mechanical properties of the nanocomposite are getting worse. This article reports the results of computer simulations for predicting the mechanical properties of polymer/single-walled carbon nanotube nanocomposites. The efficiency of reinforcing polymer composites is considered depending on the concentration of carbon nanotubes in the polymer matrix, their size and structure. The elastic moduli of the nanocomposites were predicted using computer simulations for unit cell tension (0.1%). General trends in the mechanical properties of composites with polypropylene, poly (ethyl methacrylate), polystyrene matrices and SWCNTs are shown.

The fluorinated organic compounds have superior physicochemical properties than general organic compounds due to the strong C-F single bond, and widely used in medicine, biology, pesticides, and materials science. In order to gain a deeper understanding of the physicochemical properties of fluorinated organic compounds, fluorinated aromatic compounds have been investigated by various spectroscopic techniques. 2-Fluorobenzonitrile and 3-fluorobenzonitrile are important fine chemical intermediates, and their excited state S1 and cationic ground state D0 vibrational features remain unknown. In this paper, we used two-color resonance two photon ionization (2-color REMPI) and mass analyzed threshold ionization (MATI) spectroscopy to study S1 and D0 states vibrational features of 2-fluorobenzonitrile and 3-fluorobenzonitrile. The precise excitation energy (band origin) and adiabatic ionization energy were determined to be 36028 2 cm-1 and 78650 5 cm-1 for 2-fluorobenzonitrile; and 35989 2 cm-1 and 78873 5 cm-1 for 3-fluorobenzonitrile, respectively. The density functional theory (DFT) at the levels of RB3LYP/aug-cc-pvtz, TD-B3LYP/aug-cc-pvtz, and UB3LYP/aug-cc-pvtz were used to calculate the stable structures and vibrational frequencies for the ground states S0, excited state S1, and cationic ground state D0, respectively. Franck-Condon spectral simulations for transitions of S1 S0 and D0 S1 were performed based on above DFT calculations. The theoretical and experimental results are in good agreement. The observed vibrational features in S1 and D0 states are assigned according to the simulated spectra and the comparison with structurally similar molecules. Several experimental findings and molecular features were discussed in detail.

On the aero-engine combustor liner, a phenomenon of overheating resembling a hot spot exists, accompanied by a preferential peeling of the YSZ thermal barrier coating that will negatively affect the service life of the component. The temperature gradient of the hot spot may affect the YSZ fracture pattern.In this paper, the morphological and property changes of YSZ sintering is investigated experimentally, and a coupled analysis of the YSZ Crack Propagation Mode under the hot spot is performed using ABAQUS. The results show that the pore fractal size D of YSZ increases by 15%. Inside the hot spot region, the coating has a shear stress of 75MPa. By inserting Cohesive units globally in the model to simulate the random crack initiation and propagation, unlike the thinning of YSZ layered peeling caused by uniform superheating, hot spot will cause the crack initiation at the tip of the pores inside the coating and the oblique propagation, eventually forming an oblique crack connection through the pores. When the temperature gradient reaches 30K/mm, the crack propagation is 40% greater than in a uniform temperature field; consequently, the YSZ in the hot spot boundary region with a greater temperature gradient results in more severe bulk peeling.

Wearable thermoelectric generators (w-TEGs) convert thermal energy into electrical energy to realize self-powering of intelligent electronic devices, thus reducing the burden of battery replacement and charging, and improving the usage time and efficiency of electronic devices. Through finite element simulation, this study successfully designed high-performance thermoelectric generator and made it into wearable thermoelectric module by adopting “rigid device - flexible connection” method. It was found that higher convective heat transfer coefficient on cold-end leads to larger effective temperature difference and better power generation performance of device in typical wearable scenario. Meanwhile, at same convective heat transfer coefficient on the cold-end, longer TE leg length leads to larger temperature difference established at both ends of device, larger device output power and open-circuit voltage. However, when the convective heat transfer coefficient increases to a certain level, optimization effect of increasing TE leg length on device power generation performance will gradually diminish. For devices with fixed temperature difference between two ends, longer TE leg length leads to higher resistance of TEG, resulting in lower device output power but slight increase in open-circuit voltage. Finally, sixteen 16×4×2 mm2 TEGs (L=1.38 mm, W=0.6 mm) and two modules were fabricated and tested. At hot end temperature Th=33 ℃ and cold end temperature Tc=30 ℃, the actual maximum output power Pout of TEG is about 0.2 mW, and the actual maximum output power Pout of TEG module is about 1.602 mW, which is highly consistent with the simulated value. This work brings great convenience to research and development of wearable thermoelectric modules and provides new, environmentally friendly and efficient power solution for wearable devices.

Fracture of viscoplastic materials is a complex process due to its time-dependent and plastic responses. Numerical simulation for fracture plays a significant role in crack prediction and failure analysis. In recent years, the phase field model is becoming a competitive approach to predict crack growth and has been extended to inelastic materials such as elasto-plastic, viscoelastic and viscoplastic materials, etc. However, the contribution of inelastic energy to crack growth is seldom studied. For this reason, we implement the phase field model coupled with a viscoplastic constitutive in finite element framework, in which the elastic energy and inelastic energy are used as crack driving energy. The implicit algorithm for a viscoplastic constitutive is presented, this procedure is suitable for other viscoplastic constitutive relations. The strain rate effect, creep effect, stress relaxation effect and cyclic loading responses are tested using a single element model with different inelastic energy contributions; A titanium alloy plate specimen and a stainless-steel plate specimen under tension are studied compared with the experimental observations in existing literature. The results show that the above typical damage phenomenon and fracture process can be well reproduced; The inelastic energy significantly affects the force-displacement responses but has slight effect on the crack paths.

Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

In this paper a combined Laplace transform (LT) and boundary element method (BEM) is used to find numerical solutions to problems of anisotropic functionally graded media which are governed by the transient diffusion-convection-reaction equation. First, the variable coefficients governing equation is reduced to a constant coefficients equation. Then, the Laplace-transformed constant coefficients equation is transformed a boundary-only integral equation. Using a BEM, the numerical solutions in the frame of Laplace transform may then be obtained from this integral equation. Then the solutions are inversely transformed numerically using the Stehfest formula. Some problems considered are those of compressible or incompressible flow, and of media which are quadratically, exponentially and trigonometrically graded materials. The results obtained show that the analysis used to transform the variable coefficients equation into the constant coefficients equation is valid, and the mixed LT-BEM is easy to implement for obtaining the numerical solutions. The numerical solutions are verified by showing their accuracy and steady state. For symmetric problems, the symmetry of solutions is also justified. Moreover, the effect of the anisotropy and inhomogeneity of the material on the solutions are also shown, as to suggest that it is important to take the anisotropy and inhomogeneity into account when doing experimental studies.

Breast and Cervical cancer are significant causes of morbidity and mortality among women worldwide. Usually, conventional therapies and early detection has shown success in metastasis and relapse rates and improved overall survival of the patients. The elimination of the efficacy of chemotherapies and target therapies because cancer stem cells create resistance against chemotherapy, metastasis, and relapse. Studies reveal that cancer stem cell markers are important for improving efficiencies of targeted therapies. Our study aims to analyze the stem cell markers and target the most significant stem cell marker by phytochemicals and their derivates. Through the analysis, it was observed that CXCR4 and SOX-2, mir-1, mir-125, and mir-429 may have a crucial role in stemness of breast and cervical cancer respectively, while BPMD & MD simulations revealed Di-valinoyl curcumin as a potential compound for CXCR4 targeting.

Hydrogel microneedles are a promising technology for the delivery of different types of medicines locally and painlessly, as well as ISF extraction. As the hydrogel microneedles are inserted into the tissue, they swell and release drugs. To improve the effectiveness of this technology in delivering medicine at controlled and desirable doses and intervals, a deep understanding of the mechanism of drug delivery inside the microneedles is required. In this work, drug diffusion inside a tapered microneedle is investigated using numerical simulation. The microneedle is divided into many small elements, and the mass transfer equation of meloxicam is solved in each element over time. The skin is simulated as the sink in the microneedle surface for drug absorption. Simulations are performed for different sizes of microneedles. For a microneedle with a height of 500 µm and a base diameter of 250 µm, the drug completely penetrates the skin within 3.2 seconds. The rate of drug diffusion from the tip of the microneedle is higher than diffusion from the side area near the microneedle base. The obtained data demonstrate that in addition to the height and the base diameter, the microneedle’s aspect ratio, h/d, also affects the time of drug diffusion. We present a nonlinear equation to predict the time of complete drug diffusion as a function of the microneedle geometrical parameter, including the height and base diameter. The proposed equation calculates the total drug diffusion time with an error of less than 7% for all studied cases. Predicting drug diffusion patterns inside microneedles can be helpful in the biomedical field, especially in the drug-controlled release system for the optimization of drug delivery.

Dynamic Realistic Failure Process Analysis (DRFPA) is a numerical software based on FEM it is firstly validated by comparing with the experimental test. Such validation is carried out under two consistent concrete specimens under uniaxial compression. After validation, DRFPA is used to study the influence of two parallel pre-existing crack inclination angles on the fracture of concrete under dynamic loads. The evolution of wing cracks and secondary cracks is investigated by observing the initiation, coalescence, and propagation of them. Meanwhile, the relationship between failure mode and inclination angles is discussed via analyzing the acoustic emission events. Finally, it is concluded the relationship between inclination angles of pre-cracks and performance/stability of concrete structure.

Hospital Pulau Pinang is the general hospital in Malaysia which targeting energy savings of 10% within five years from 2015 and other sustainability targets such as 3-star Energy Management Gold Standard and Green Building Certification. The targets are beneficial for the hospital itself to establish the Smart Building Program to improve its energy efficiency concurrent with the green policy of the Ministry of Health Malaysia and Sustainable Development Goals by the United Nations. This paper reviews the background of Hospital Pulau Pinang energy data , energy consumption trending, energy-saving trending, and energy conservation measures taken for the hospital from 2015 to December 2021.The yearly energy consumption baseline taken in 2016 was 27,496,731.00 kWh. It reduced significantly to 21,356,063 kWh in 2021 due to energy conservation measures. As a result, Hospital Pulau Pinang has achieved energy-saving about 16% at approximately RM7.3 million reduction in operational expenditure. The main objective of this paper is to provide further potential energy savings by studying the energy reduction by implementing solar photovoltaics using the simulation method. The simulation method can predict that Hospital Pulau Pinang can achieve another 5,130,000 kWh energy savings annually. This type of simulation has never been done before at a public hospital, and it will give further enhancing strategies to the Smart Building Program itself. Furthermore, the potential of smart building can be maximized to the next level by simulation, which helps the hospital energy committee make the potential decision on the energy-saving investment.

For the numerical simulation of components made of short fiber-reinforced composites the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study numerical simulations of tensile test specimens are conducted utilizing a finite deformation elastic-ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach tensile tests until failure are carried out experimentally, that confirm the assumption of spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations are performed for different values of the correlation length. The numerical simulations endorse the influence of the correlation length on the overall behavior. For a correlation length of 5mm a good conformity with the experimental results is obtained. Therefore, it is concluded, that the presented modeling approach is suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.

Emergence of multi drug resistant (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis poses a serious threat to TB control as the available treatment options are less effective in these cases. Therefore, new strategies are required for identification of new drugs and drug targets. The M. tuberculosis dihydrodipicolinate synthase is a putative drug target with no potent inhibitor. Here in this study, we have used a comprehensive computational approach to identify small molecule inhibitors and validated them in vitro. As similar structures tend to have similar functions, therefore 3 approaches, namely, (a) known inhibitor-based approach, (b) substrate analogues, (c) product analogues were used for compound screening from various chemical libraries. The fourth approach we have taken is fragment library screening.In vitro studies revealed that β-hydroxypyruvic acid tartronic acid are inhibitors of Mtb-rDapA with maximum inhibition 48% and 51% at 500µM concentrations respectively. We have also observed that β-hydroxypyruvic acid is a time dependent inhibitor of Mtb-rDapA. Further, thermal shift assays showed that β-hydroxypyruvic acid interacts with Mtb-rDapA and the thermal stability of Mtb-rDapA shifted by 3˚C. Among the product analogues of Mtb-DapA, dipicolinic acid showed to bind to Mtb-rDapB shifting thermal stability of Mtb-rDapB by 0.5˚C in binding. Additionally, we have designed a new approach by combining substrate and product analogues to achieve better inhibitions. In vitro assays validated that this combination approach increased maximum inhibition up to 100%. We envision that these compounds would serve as potent leads for tuberculosis drug discovery.

It is well established that the interacting effects of temperature and precipitation will alter agroecological systems on a global scale. These shifts will influence the fitness of specialty crops, specifically strawberries (Fragaria x ananassa), an important crop in the Northeastern United States. In this study, four precipitation scenarios were developed that are representative of current and probable-future growing season precipitation patterns. Using a precipitation simulator, we tested these scenarios on potted day neutral strawberries. This study generated four primary results: (1) though treatments received different amounts of precipitation, little difference was observed in soil volumetric water content or temperature. However, treatments designed to simulate future conditions were more likely those designed to simulate current conditions to have higher nitrate-in-leachate (N-leachate) concentrations; (2) neither total precipitation nor seasonable distribution were associated with foliar or root disease pressure; (3) while there was a slightly higher chance that photosynthetic potential and capacity would be higher in drier conditions, little difference was observed in the effects on chlorophyll concentration, and no water stress was detected in any treatment; and (4) leaf biomass was likely more affected by total rather than seasonal distribution of precipitation, but interaction between changing rainfall distribution and seasonal totals is likely to be an important driver of root biomass development in the future.

As use of composite materials increases, the search for suitable automated processes gains relevance to guarantee production quality by ensuring uniformity of the process, minimizing the amount of generated scrap and reducing time and energy consumption. Limitations on production by traditional means such as hand lay-up, vacuum bagging and in-autoclave methods, tend not to be as efficient when the size and shape complexity of the part being produced increases, motivating the search for alternative processes such as the Automated Tape Laying (ATL). This work aims to describe the process of modelling and simulating a composite ATL with in situ consolidation by characterizing the machine elements, using the finite differences method in conjunction with energy balances, in order to create a digital twin of the process for further control design. The modelling approach implemented is able to follow the process dynamics when changes to the heating element are imposed as well as to predict the composite material temperature response, making it suitable to work as a digital twin of a production process using an ATL machine.

Background Augmented reality (AR) in surgery can offer an enhanced view of reality through the superimposition of computer-generated digital images on the real environment. It allows surgeons to integrate image visualisation, improving operative efficiency, surgical outcomes, surgical training and patient education. This review aims to evaluate the current status of augmented reality in surgery, surgical training and potential future applications. Methods We performed a non-systematic review of available literature from January 2005 to August 2021 by searching PubMed, EMBASE and the Cochrane library using a combination of terms “augmented reality”, “virtual reality”, “surgery”, “simulation” and “training”. Articles considered for this review were identified by relevant search criteria including title, keywords, abstract, and full-text. Conclusions AR technologies present an exciting new trend with multiple potential applications in surgery. Intraoperative AR systems have shown promise in specialties involving fine movement of organs during surgical procedures, including Neurosurgery, Ears, Nose and Throat and Orthopaedic Surgery. AR has also exhibited the potential to enhance surgical training and improve knowledge acquisition; it can foster international collaborations via telesurgery and telepresence. In the near future, AR will likely work in symbiosis with surgeons, serving as a complex computer-human coalition which can improve patient outcomes, patient education and surgical training.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

Background: Simulations have historically aided training programs by providing a realistic and holistic replication of professional scenarios and procedures. Simulations have developed over the past 40 years to include varying fidelities and modalities of simulation. Learning in a simulation-centered environment has benefits, ranging from improved patient care to specific skills acquisition while catering to students’ numerous and varied learning approaches. Simulation is a multifaceted field that benefits all parties, the teachers, the learners, and the patients. The application of simulation to medical education and its amalgamation with other modes and substitutes allows for a more integrated learning and testing curriculum that advances the current trajectory of medical education. Such developments, however, are limited to resource rich areas, leaving behind low-middle income countries to use traditional, less evolved methodologies and practices. This review aimed to explore different aspects of simulation and focus specifically on low-cost task trainers and their accessibility. Method: The purpose of the study was to assess the accessibility of low-cost task trainers in terms of cost-effectiveness, distribution, validation, and frequency within specific specialties. To do so, 84 PubMed publications have been screened, and 39 filtered research studies have collected the necessary data. After analyzing the papers, we classified the following information – process, specialization, validation (y/n), costs, development location, and year of publication. Results: After carefully analyzing the accumulated data from the selected 39 publications, we found that most studies (i.e., 6 out of 39) were published in 2020. Emergency Medicine was the most common specialty for which low-cost trainers were developed (9 out of 39 procedural simulators); Otolaryngology followed this with 8 out of 39 trainers and general surgery with 7/39 of the task-trainers. The price ranges fluctuated and fell within the price bracket of USD 0 to USD 400 collectively. Our review also uncovered the concentration of development of such innovations solely in high income countries (HICs). Conclusion: Simulation is an invaluable tool applicable to a diverse range of phases of medical education. Future conjunction of simulation with low-cost substitutes along with increased encouragement and enthusiasm towards developing cost effective simulation-based learning environments (SBLEs) with the reserves and requirements of these areas in mind may prove to be a reliable option for low and middle resource settings

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

Overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma . Predominately, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS) -induced apoptosis. Here, we showed a novel link between BRF2 and DNA damage response. Due to the lack of BRF2 specific inhibitors, through virtual and molecular dynamics screening, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested Bexarotene as a potential BRF2 inhibitor. We found that Bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress (Tert-butylhydroquinone (tBHQ))-induced levels of BRF2 and consequently, lead to a decrease in cellular proliferation of cancer cells which may in part be due to drug pretreatment induced reduction of ROS generated by the oxidizing agent. Our data thus, provide the first experimental evidence that BRF2 is a novel player in DNA damage response pathway and Bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Methods based on simulation pedagogy are widely used to practice hands on skills in safety environment. The usability of an EEG-simulator on clinical neurophysiology course was evaluated. Second year biomedical laboratory science students (N=35) on this course was included in the study. They were divided into three groups. Two groups used the EEG simulator with different feedback modes and one group without use of the simulator. Results was expected to reveal a correlation between user experience and learning outcomes. This study made used of a mixed method study design. During the study students were asked to keep a learning diary throughout the course on their experience. Diaries were analyzed qualitatively based on content analyses. Quantitative analyses based on an UX questionnaire that measures classical usability aspects (efficiency, perspicuity, dependability) and user experience aspects (novelty, stimulation) and the students’ feelings to use simulator. The quantitative data was analyzed using SPSSTM software. The quantitative and qualitative analyses showed that the use of EEG-simulator which was evaluating teaching-learning process have an extra benefit in clinical neurophysiology education and students felt that simulator is useful in learning. The simulation debriefing session should be followed by a full theoretical and practical session. Students compare their learning from the simulator with that of the actual placement which fosters the reflective practice of learning again deepening the understanding of the EEG electrode placement and different wave patterns.

The conformational state of the activation loop (A-loop) is pivotal for the activation of most protein kinases. Hence, the characterization of the conformational dynamics of the A-loop is important to increase our understanding of the molecular processes related to diseases and to support the discovery of small molecule kinase inhibitors. Here, we carry out a combination of Molecular Dynamics (MD) and Essential Dynamics (ED) analyses to fully map the effects of phosphorylation, ADP, and conformation disrupting (CD) inhibitors (i.e., CD532 and MLN8054) on the dynamics of the A-loop of Aurora-A. MD revealed that the stability of the A-loop in an open conformation is enhanced by single phospho-Thr-288, while paradoxically, the presence of second phosphorylation at Thr-287 decreases such stability and renders the A-loop more fluctuant in time and space. Moreover, we found that such post-translational modification has a significant effect on the direction of A-loop motions. ED analysis suggests that the presence of the phosphate moiety induces the dynamics of Aurora-A to sample two distinct energy minima, instead of a single large minimum, as in unphosphorylated Aurora-A states. This suggests that the conformational distributions of Aurora-A with both single and double phospho-threonine modifications are remarkably different from the unphosphorylated state. In the closed states, binding of CD532 and MLN8054 inhibitors has the effect of increasing the distance of the N- and C-lobes of the kinase domain of Aurora-A, and the angle analysis between those two lobes during MD simulations showed that the N- and C-lobes are kept more open in presence of CD532, compared to MLN8054. As the A-loop is a common feature of Aurora protein kinases, our studies provide a general description of the conformational dynamics of this structure upon phosphorylation and different ligands binding.

Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though a lot and research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from working with district heating and cooling projects, along with relevant research, corresponding research gaps and proposed research directions.

This research evaluates the inhibitory effect of L-amino acids (AAs) with different side chain lengths on Fe surface implementing Monte Carlo (MC) simulation. A quantitative and qualitative description of the adsorption behavior of AAs on the iron surface has been carried out. Calculations have shown that the absolute values of the adsorption energy of L-amino acids increase with side chain prolongation; they are also determined by the presence of heteroatoms. AAs from nonpolar and basic groups have the best adsorption ability to the iron surface, which indicates their highest inhibitory efficiency according to the results of MC simulation.

This paper examines the influence of physical parameters on the collapse dynamics of a spherical bubble filled with diatomic gas ($\kappa=7/5$). The present numerical investigation shows that each physical parameter affects the bubble collapse dynamics differently. After comparing the contribution of each physical parameter, it appears that, of all the parameters, the surrounding liquid environment affects the bubble collapse dynamics the most. Meanwhile, surface tension has the weakest influence and can be ignored in the bubble collapse dynamics. However, surface tension must be retained in the initial analysis since this, as well as the pressure difference jointly control initial bubble formation. As an essential part of this study, a general Maple code is provided.

In the process of metal cutting, the anti-wear performance of the tool determines the life of the tool and affects the surface quality of the workpiece. The finite element simulation method can directly show the tool wear state and morphology, but due to the limitations of the simulation time and complex boundary conditions, it has not been commonly used in tool life prediction. Based on this, a tool wear model was established on the platform of a finite element simulation software for the cutting process of titanium alloy TC4 by end milling. The key technique is to embed different types of tool wear models into the finite element model in combination with the consequent development technology. The effectiveness of the tool wear model is validated by comparing the experimental results with the simulation results. At the same time, in order to quickly predict the tool life, an empirical prediction formula of tool wear was established, and the change course of tool wear under time change was obtained.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

There are ~200.000 households living in the detached houses and ger (yurt) with the small-coal stove which burns a raw coal in the Ulaanbaatar city. The proper heating system and improvement on the energy efficiency of residential dwellings is vital important for Ulaanbaatar city to reduce an air pollution as well as for the operation of current central energy system. This study shows the experimental results of two Gers with two different heating system and different thermal insulation. Technical availability of the system consisting of ETS heater with daytime charging schedule and areal PV system is also examined by using a simulation of MATLAB programming. As a result of the experiment, the indoor comfort level and energy efficiency of the Ger with added insulation and ETS heater with nighttime charging was enhanced to compare with the reference Ger. The Ger with added insulation and ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation was resulted that the ETS heater to be installed in the Ger 2 had prospected to increase the PV self-consumption rate by 76% because of the daytime charging schedule of ETS heater. The PV system supplied 31% of the total energy consumption with remained 69% of the main grid.

The purpose of this paper is to simulate a two-dimensional Rayleigh-Taylor instability problem using the classical method of Finite Element analysis of a multiphase model using ANSYS FLUENT 19.2. The governing equations consist of a system of coupled nonlinear partial differential equations for conservation of mass, momentum and phase transport equations. The study focuses on the transient state simulation of Rayleigh Taylor waves and subsequent turbulent mixing in the two phases incorporated in the model. The Rayleigh Taylor instability is an instability of an interface between two fluids of different densities which occurs when the lighter fluid is pushing the heavier fluid in a gravitational field. The problem was governed by the Navier-Stokes and Cahn-Hilliard equations in a primitive variable formulation. The Cahn- Hilliard equations were used to capture the interface between two fluids systems. The objective of this article is to perform grid dependency test on Rayleigh Taylor Instability for 2 different mesh size and compare the results for the variation in Atwood Number. The results were validated with the observations from previous published literatures.

In order to reveal the mechanism and influencing factors of high pressure jet cavitation of nozzle in submerged environment, this study focused on the evolutionary process of cavitation bubbles and combined finite volume method and mixed multi-phase flow model to analyze the cavitation, velocity distribution and experimental cavitation intensity of fishing net cleaning equipment. Results show that the cavitation inception, growth and collapse mainly occurred in the peripheral region of the flow field. Ring-shaped cavitation erosion zone appeared on the test sample target. A lot of small dense erosion pits were densely distributed in the ring-shaped erosion zone, erosion marks were observed in the center. The cavitation erosion intensity was greatly affected by the nozzle structure. As the diameter of nozzle increased from 0.6 mm to 1 mm, the maximum gas volume fraction increased by 8.53%. The nozzle outlet enlargement angle greatly increased the cavitation intensity. The nozzle with an outlet angle of 30° exhibited the optimal cavitation erosion performance . The cavitation volume fraction of the nozzle with short necking structure was slightly larger than that of the nozzle with long necking structure at the same level in the necking length rang of 3mm to 7mm. In terms of the influence of nozzle structure on the cavitation erosion effect, the nozzle diameter D ranked the first, followed by the outlet angle α, and the necking length L was at the last.

Binder jet printed components typically have low overall density in the green state and high shrinkage and deformation after heat treatment. It has previously been demonstrated that, by including nanoparticles of the same material in the binder, these properties can be improved as the nanoparticles can fill the interstices and pore throats between the bed particles. The beneficial effects from using these additive binder particles can be improved by maximising the binder particle size, enabling the space within the powder bed to be filled with a higher packing efficiency. The selection of maximum particle size for a binder requires detailed knowledge of the pores and pore throats between the powder bed particles. In this paper, a raindrop model is developed to determine the critical radius at which binder particles can pass between pores and penetrate the bed. The model is validated against helium pycnometry measurements and binder particle drop tests. It is found that the critical radius can be predicted, with acceptable accuracy, using a linear function of the mean and standard deviation of the particle radii. Percolation theory concepts have been employed in order to generalise the results for powder beds that have different mean particle sizes and size distributions. The results of this work can be employed to inform the selection of particle sizes required for binder formulations, to optimise density and reduce shrinkage in printed binder jet components.

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

Two-dimensional arrays of hollow nanotubes made of TiO2 are a promising platform for sensing, spectroscopy and light harvesting applications. Their straightforward fabrication via electrochemical anodization, growing nanotube pillars of finite length from a Ti foil, allows precise tailoring of geometry and, thus, material properties. We investigate these photonic crystal structures with respect to reduction of front surface reflection, achievable field enhancement, and photonic bands. With the Rigorous Coupled Wave Analysis (RCWA), we study the optical response of photonic crystals made of thin-walled nanotubes relative to the bare Ti foil substrate, including under additional charge carrier doping.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

Micro-pollutants such as 17β-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.

Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.

This paper deals with the problem of real-time management of Smart Grids. For this sake, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. The proposed technique is tested with the help of an academic microgrid, so the results may be replicated.

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

To regulate the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply-demand balance, the government can adjust BEV regulations and offer subsidies. The Jawa-Madura-Bali (Jamali) electrical system, being the largest in Indonesia [1], faces the challenge of accommodating the increasing number of vehicles [2]. Subsequent to analyzing Jamali's electricity supply using data from the National Electricity Company (RUPTL) [3], simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, internal combustion engine (ICE) and electric vehicle populations, initial charging time (ICT), slow- and fast-charging ratios, and BEV charge load curves are simulated. Scenario variables like supply capacity growth speed, vehicle population growth speed, subsidy impact on EV attractiveness, ICT, and fast-charging ratio are subsequently simulated for 2024-2029. Four key simulation outcomes are identified: the best-case scenario (Scenario 1776), achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst outcome scenario leads to a 23.12% EV percentage, necessitating 47.566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on literature research are evaluated. This simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029 [4], aiding the government in planning regulations and subsidies effectively. Improving the simulation's accuracy depends on refining input variables over time.

This qualitative study explores the role of supply chain partnerships in enhancing brand value through collaborative marketing efforts. By analyzing diverse cases from industries including consumer electronics, apparel, automotive, and food and beverage, the research examines how different forms of partnerships—strategic alliances, co-marketing, joint ventures, and supplier integrations—contribute to brand value. The study employed a multiple case study design, incorporating semi-structured interviews, document analysis, and participant observations to gather data. The findings reveal that these partnerships enable companies to leverage complementary strengths, resulting in enhanced innovation, product quality, sustainability, market reach, and ethical standards. Collaborative marketing strategies such as joint product launches, sustainable branding campaigns, and co-developed customer programs are identified as key drivers of brand value, effectively increasing brand awareness, customer loyalty, market share, and perceived quality. Despite challenges like coordination complexity, goal misalignment, cultural differences, and cost management issues, the benefits realized—such as improved brand image, faster time-to-market, and reduced costs—demonstrate the substantial value of effective supply chain partnerships. The study underscores the importance of strategic alignment between supply chain activities and brand objectives, highlighting that successful partnerships foster deeper consumer engagement and stronger market positions. This research provides actionable insights for companies seeking to optimize their supply chain collaborations to enhance brand value, offering a foundation for future studies and practical applications in supply chain management and marketing. The findings contribute to a deeper understanding of how supply chain partnerships can be strategically managed to drive brand excellence in a competitive market environment.

Background: Pediatric forearm fractures represent a substantial proportion of childhood injuries, requiring effective and minimally invasive treatments. Our study investigated the mid-term outcomes of biodegradable poly-L-lactide-co-glycolide (PLGA) intramedullary implants in managing diaphyseal forearm fractures in children. Methods: A follow-up cohort study was conducted with 38 patients treated with PLGA implants. Control examinations were performed one year post-operation, assessing bone healing through radiographic evaluations and functional outcomes using injured and uninjured limb range of motion (ROM) comparisons. Scarring was evaluated employing the Vancouver Scar Scale (VSS), and satisfaction via a questionnaire. Results: Children were predominantly female (76.4%), with a mean age of 9.71 (SD: 2.69) years. Effective fracture stabilization and bone healing were found in all patients, with a minor reduction (mean difference of -1.5°, p = 0.282) in elbow flexion on the operated side (139.3°) compared to the intact (140.8°). Elbow extension presented negligible average changes (0.2°, p= 0.098). Forearm movements were slightly reduced on the operated side (mean pronation: 80.8° vs. 83.7°, p= 0.166; average supination: 83.5° vs. 85.7°, p= 0.141). Wrist palmar flexion and dorsiflexion showed no significant differences. VSS ratings indicated minimal scarring (mean guardian and doctor scores were 1.13 and 0.55, respectively, p= 0.020), and all patients reported satisfaction with the treatment outcomes. Conclusion: Biodegradable implants are effective for pediatric forearm fractures, providing stable bone healing while preserving functional ROM with minimal scarring and high patient satisfaction. PLGA proved to be a viable alternative to traditional metal implants, eliminating secondary removal surgeries.

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e. sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago the ratio between emitted and sequestered carbon has varied considerably with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with domestication of crops and livestock leading to urbanisation and development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance it is important to understand the roles of high-capacity carbon sequestration systems, such as natural forests and tropical croplands, and to devise strategies to facilitate net CO₂ uptake.

Currently, there is a tenacious need to find new ways to purify water by eliminating bacterial biofilms, textile dyes, and toxic water pollutants. These contaminants pose significant risks to both human health and the environment. To address this issue, in this study, we have developed an eco-friendly approach that involves synthesizing Cobalt-doped Cerium Iron Oxide (CCIO) nanocomposite (NC) using an aqueous extract of Gossypium arboreum L. stalks. The resulting nanoparticles can be used to effectively purify water and tackle the challenges associated with these harmful pollutants. Nanoparticles excel in water pollutant removal by providing high surface area for efficient adsorption, versatile design for simultaneous removal of multiple contaminants, catalytic properties for organic pollutant degradation, and magnetic features for easy separation, offering cost-effective and sustainable water treatment solutions. CCIO nanocomposite has been synthesized by green co-precipitation method due to biomolecules and co-enzymes present in the aqueous extract of Gossypium arboreum L. stalk; a single-step procedure completed in 5 h reaction time. Further, the synthesis of nanocomposites was confirmed by various characterization techniques such as Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), Thermogravimetric analysis (TGA), Dynamic light scattering (DLS), and Energy Dispersive X-Ray (EDX). CCIO NCs were discovered to have a spherical shape and an average size of 40 nm. Based on DLS zeta potential analysis, CCIO NCs were found to be anionic. Also, CCIO NCs showed significant antimicrobial and antioxidant activity. Overall, considering the physical and chemical properties, the application of CCIO NCs in the adsorption of various dyes (~91 %) and water pollutants (chromium = ~ 60 %) has been considered since they exhibit great adsorption capacity owing to their microporous structure which can be a step forward in water purification.

This research explores the behaviors of Generation Z consumers in China, particularly focusing on their interaction with companies through social media for the joint creation of value. Adopting a qualitative methodology, the research initially constructs a scale for assessing co-creation of value on social media platforms. Subsequently, it delves into the motivational aspects of users' engagement in these co-creative activities. The study unveils a range of utilitarian and hedonic motivations that drive Generation Z's value co-creation actions on social media. Insights derived from the findings offer both theoretical and practical implications for the field.

Viral co-infections, where a host is infected by multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus-virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for identifying viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections, and underscores the need for interdisciplinary collaborations to address this complex and important topic.

Spinocerebellar ataxia type 3 is a neurodegenerative disease caused by an abnormal expansion of CAG repeats in the disease gene ATXN3, leading to prolonged polyglutamine (polyQ) tracts in the respective protein ataxin-3. The polyQ-expanded protein forms polyQ-containing aggregates in several brain regions which leads to neuronal cell loss. Autophagy is thought to play a significant role in clearing these polyQ-containing protein aggregates. Therefore, the autophagy mechanism was investigated in SCA3 with focus on the autophagic protein p62/sequestosome-1. Human and mouse RNA sequencing data were analyzed to determine RNA expression level of autophagic genes. Additionally, autophagy was studied in the SCA3 304Q knock-in mouse model and through several cell culture experiments using HEK-293T cells transfected with ATXN3 of differ-ent polyQ lengths. For cell culture experiments, autophagy was induced with rapamycin treat-ment, proteasomal degradation was blocked with MG132 and degradation of autophagic pro-teins was investigated using cycloheximide treatment. Here, we report an increased autophagy impairment with SCA3 disease progression seen in HEK-293T cells transfected with ATXN3 plasmid with expanded polyQ tracts, higher aged 304Q-knock-in mice, or human post-mortem brain material. Furthermore, co-localized aggregation of the autophagic protein p62 and the dis-ease protein ATXN3 was observed in the course of SCA3 disease. Therefore, we suggest a signifi-cant role for p62 in aggregate formation seen in the neurodegenerative disease SCA3.

This study investigates the influence of economic growth on CO2 emissions levels in three developed countries—Argentina, Equatorial Guinea, and South Korea—based on their status as high-income countries. We test the environmental Kuznets curve (EKC) hypothesis using the time series approaches to check the relationship and direction of causality among the variables over a sample spanning 1974–2020. The results show an inverted U-shaped short-run relationship in all three countries. In the long run, only South Korea supports the EKC hypothesis. Further, Granger causality results indicate the existence of causality. These long-run causal relationships between economic growth and emissions recommend one policy implication that wealthy governments must expand investments in renewable clean energy projects and R&D, with regulatory measures to suppress harmful environmental procedures and support environmentally friendly development.

During the COVID-19 pandemic, the circulation of non–SARS-CoV-2 respiratory viruses changed throughout the world. This study aimed to analyze the effect of respiratory syncytial virus (RSV), metapneumovirus (MPV), rhinovirus (RV), and parainfluenza (PIV)-3 single infection, dual co-infection, and viral load on cytokine and chemokine expression in 300 Mexican patients with ARIs negative for SARS-CoV-2. Molecular detection and viral load were performed by RT-qPCR. Cytokine and chemokine expression was determined in samples positive for single RSV, MPV, RV, or PIV-3 infection, and in the most frequent dual co-infections, by a cytokine panel using a magnetic-bead-based multiplex immunoassay. Of the patients positive for viral infection, 91.7% had a single infection and 8.3% had dual co-infections. In this study, RSV was the respiratory virus with the highest prevalence, and the most common dual co-infection was RV+RSV. On the other hand, RV infections had the highest viral loads compared to the other viruses detected. FGF basic, MIP-1α IL-8, IP-10, MCP-1, IFN-γ, and RANTES showed differential expression between single infections and dual co-infections compared to healthy patients. We report, for the first time, the circulation of non-SARS-CoV-2 viruses in the south of Mexico and the cytokine and chemokine profiles associated with these viruses.

A bridged 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) derivative (PN-DOPO) in combination with aluminum phosphates coated sepiolite (Sep@AlPO4) was used to improve the flame retardancy, thermal stability and mechanical properties of poly (ethylene oxide) (PEO)/poly (butylene adipate-co-terephthalate) (PBAT) blends. The synergistic effects of PN-DOPO and Sep@AlPO4 on the fire resistance, thermal decomposition behavior and mechanical properties of PEO/PBAT composites were investigated systematically. The results indicated that 5 wt % Sep@AlPO4 combined with 10 wt% PN-DOPO into PEO/PBAT achieved a V-1 rating in UL-94 test and the LOI value increased to 23.7%, meanwhile, the peak-heat release rate (p-HRR), av-HRR and total heat release (THR) values of PEO/PBAT/PN10%/Sep5% composites decreased by 35.6%, 11.0% and 23.0% compared with those of PEO/PBAT, respectively. The thermogravimetric analysis (TGA) results confirmed that PN-DOPO/Sep@AlPO4 could enhance the initial thermal stability and char yield of PEO/PBAT matrix, and TGA/Fourier transform infrared spectrometry (TGA-FTIR) results revealed that the composites exhibited the absorption peaks of phosphorous-containing groups and an increase in gas-phase volatiles during the thermal decomposition process. The digital photographs and morphological structures of char residues demonstrated that PN-DOPO and Sep@AlPO4 mixtures benefited to the formation of a more continuous and dense carbon layer on the composites surface during combustion. Rheological behavior revealed that a higher storage modulus, loss modulus and complex viscosity of PEO/PBAT/PN-DOPO/Sep@AlPO4 composites could also promote the cross-linking structure during burning. Furthermore, the PEO/PBAT/PN-DOPO/Sep@AlPO4 composites exhibited excellent mechanical properties in terms of elongation at break and flexural performance than the PEO/PBAT system. All the results demonstrated that PEO/PBAT system modified by the combination of PN-DOPO and Sep@AlPO4 not only exhibited excellent synergistic effect on the flame retardance, but also had good improvement on thermal stability and mechanical properties, indicating the potential application in areas requiring fire safety.

The devastating effects of COVID-19 permeates virtually all aspects of life across geographic boundaries. As the lockdown is eased, and reinstated, across many parts of the world, attention is now shifting towards how to navigate the ‘now normal’ rather than the ‘next normal,’ of doing business with a view to achieving sustainable development. This study highlights some of the challenges and opportunities for micro, small, and medium enterprises (MSMEs) in times of crisis such as COVID-19. Conceptual by orientation, the study provides a broad overview of trends in crises times, drawing upon a range of qualitative accounts. The overarching message is the current direction of travel for MSMEs in sustaining their bottom-line, not after the crisis, but at this particular moment in time. The study highlights some implications for ‘pivoting,’ in order to cushion the impact of the pandemic on efforts to capacitate MSMEs in leveraging value co-creation with the consumers. The implications of the here and now are discussed.

K-Rb-83Kr based atomic co-magnetometer for measuring moving MEGs is theoretically studied in this paper. Parameters such as the spin exchange rates, the spin dephasing rates and the polarization of the nuclear spins are studied to configure the co-magnetometer. Results show that the nuclear spin could generate around 700 nT magnetic field under which the nuclear spin could compensate wide range of magnetic fields. We also showed the hybrid optical pumping vapor cell fabrication process in this paper. Alkali metals were mixed in a glove box and then was connected to the alkali vapor cell fabrication system. The vapor cell fabrication process is illustrated in this paper.

Transition metal alloys have emerged as promising electrocatalysts due to their ability to modulate key parameters such as d-band electron filling, Fermi level energy, and interatomic spacing, thereby influencing their affinity towards reaction intermediates. However, the structural stability of alloy electrocatalysts during alkaline hydrogen evolution reaction (HER) remains a subject of debate. In this study, we systematically investigate the structural evolution and catalytic activity of the c-Co/Co3Mo electrocatalyst under alkaline HER conditions. We reveal that the instability of the Co3Mo alloy and H0.9MoO3 during alkaline HER leads to the destruction of the crystal structure, resulting in the formation of hexagonal cobalt (h-Co) from hexagonal Co(OH)2 as an intermediate species. Furthermore, we explore the re-adsorption and surface coordination of the Mo element, which enhances the catalytic activity for alkaline HER. This work provides valuable insights into the dynamic behavior of alloy-based electrocatalysts, shedding light on their structural stability and catalytic activity during electrochemical reduction processes

The pathogenesis of porcine circovirus type 2b (PCV2b) and swine influenza A virus (SwIV) during co-infection in swine respiratory cells is poorly understood. To elucidate the impact of PCV2b/SwIV co-infection, newborn porcine tracheal epithelial cells (NPTr) and immortalized porcine alveolar macrophages (iPAM 3D4/21) were co-infected with PCV2b and SwIV (H1N1 or H3N2 genotype). Viral replication, cell viability and cytokine mRNA expression were determined and compared between single-infected and co-infected cells. Finally, 3’mRNA sequencing was performed to identify the modulation of gene expression and cellular pathways in co-infected cells. It was found that PCV2b significantly decreased and improved SwIV replication, in co-infected NPTr and iPAM 3D4/21 cells respectively, compared to single infected cells. Interestingly, PCV2b/SwIV co-infection synergistically up-regulated IFN expression in NPTr cells whereas in iPAM 3D4/21 cells, PCV2b impaired the SwIV IFN induced response, both correlating with SwIV replication modulation. RNA-sequencing analyses revealed that the modulation of gene expression and enriched cellular pathways during PCV2b/SwIV H1N1 co-infection is regulated in a cell type-dependent-manner. This study revealed different outcomes of PCV2b/SwIV co-infection in porcine epithelial cells and macrophages and provides new insights on porcine viral co-infections pathogenesis.

Escherichia coli and Shigella are common diarrhea-causing pathogens in children and adults. Enteroinvasive Escherichia coli (EIEC) shares a similar pathogenic mechanism with Shigella. However, EIEC are less virulent than Shigella. The aim of this work was to get a better understanding of the virulence differences between EIEC and S. flexneri. We investigated i) the bacterial gene co-expression networks (GCNs) and ii) the the transcriptional modules (WGCNA) of Caco-2 cells infected with EIEC or with S. flexneri during a three-hour period of bacterial infection. The GCN analysis showed that EIEC and S. flexneri networks presented different topologies. Additionally, the EIEC network revealed that pINV genes are not connected with chromosomal genes. WGCNA and eigengene analysis showed enterocyte gene expression variation along the three-hour bacterial post-infection period. Additionally, at one-hour post-infection EIEC induced a higher number of gene expression changes in Caco-2 cells than S. flexneri. Several of these genes are involved in autophagy. This study showed that the lower virulence of EIEC is associated with a lack of functional cooperation between pINV and chromosomal genes, differently from what was observed in S. flexneri. Consequently, EIEC becomes less efficient in subverting host-cell bacterial recognition as well as defense mechanisms such as autophagy.

Heat shock proteins (HSPs) are a family of molecular chaperones that regulate essential protein refolding and triage decisions to maintaining protein homeostasis. Numerous co-chaperone proteins directly interact and modify the function of HSPs, and these interactions impact the outcome of protein triage, impacting everything from structural proteins to cell signaling mediators. The chaperone/co-chaperone machinery protects against various stressors to ensuring cellular function in the face of stress. However, coding mutations, expression changes, and post-translational modifications of the chaperone/co-chaperone machinery can alter the cellular stress response. Importantly, these dysfunctions appear to contribute to numerous human diseases. Therapeutic targeting of chaperones is an attractive but challenging approach due to the vast functions of HSPs, likely contributing to the off-target effects of these therapies. Current efforts focus on targeting co-chaperones to develop precise treatments for numerous diseases caused by defects in protein quality control. This review focuses on the recent developments regarding selected HSP70/HSP90 co-chaperones, focusing on cardioprotection, neuroprotection, and cancer. We also discuss therapeutic approaches that highlight both the utility and challenges of targeting co-chaperones.

The fusion of "innovation theory" and "ecology" gave birth to a large number of studies on "innovation ecology", which mainly studies how to build an industrial ecological chain at the regional level, focusing on self-evolution, achieving ecological balance, and enabling the regional economy to take the path of sustainable innovation. This type of research borrows a lot of concepts from ecology, and very vividly describes the competition and cooperation relationships formed by various agents in the innovation system, laying a good foundation for qualitative analysis of the inherent dynamics of innovation development. However, many studies focus on the analogous description of ecosystems and economic systems, lacking scientifically and rigorously quantitative empirical research as a support. This paper uses network-based indicators such as degree, cluster coefficient, and betweenness centrality to measure the function and position of high-tech enterprises in the Z-Park in a business environment, so as to clarify the socio-economic meaning of the topological structure of the regional innovation system. On this basis, it provides theoretical references for regional innovation development and sustainable development policy formulation.

We present a normative methodology, for value-pricing B2B services, using a Nash Equilibrium mechanism. Value is fundamental to any service, yet it has defied a definition. The literature is conspicuously silent on how to define value, but abounds in richly descriptive characterizations. To overcome this deficit, we focus on the financial economics of service-value. We formulate an axiomatic definition and a differential equation that embodies the idea of service-value. We specify a set of value axioms and multidisciplinary postulates that coherently form our service-value constructs. Value-pricing is challenging. It is natural that providers desire a high price and customers want a low price. Realistically, both will agree on a win-win price. To uncover this price, we specify an algorithm to reveal the Nash Equilibrium. Once agreed, it validates providers’ and customers’ commitments, and payment obligations. Service value is cocreated by both provider and customer. For a reciprocal process, its treatment is remarkably asymmetric favoring the customer. We argue that one-sided descriptions of value-in-use and value-proposition are limiting mental models. We propose the additional ideas of value-from-use and value-supposition to strengthen the conceptual symmetry of cocreation. Our work reveals a critical gap in service science, metrology. Metrology, the science of measurements, is absent from service-science. Service-value is silent on the questions of quantities, units, scales, measurement principles and instruments. We argue for a call to action for Service Metrology. We sketch a roadmap of actionable suggestions to get started.

The extracts from the aerial parts of Helianthus salicifolius A. Dietr and Helianthus tuberosus L. collected in June were obtained using carbon dioxide supercritical fluid extraction with water as co-solvent. The antimicrobial activity in vitro of these extracts were determined against the reference species of Gram-positive and Gram-negative bacteria as well as fungi, representing by the yeast species of Candida spp. The following parameters were estimated: minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) or minimum fungicidal concentration (MFC). Both extracts were found to possess antimicrobial activity with MIC = 0.62-5 mg mL-1 for bacteria and MIC = 5-10 mg mL-1 for yeasts, showing bactericidal (MBC/MIC = 2-4) or fungicidal effect (MFC/MIC = 1-2 ). The highest activity was observed against S. aureus ATCC 29213 (MIC = 0.62 mg mL-1 for H. salicifolius extract; MIC = 2.5 mg mL-1 for H. tuberosus extract). Bactericidal effect of both extracts against S. aureus ATCC 29213 was confirmed by time-kill assay. Higher antioxidant activity was found for H. tuberosus extract (EC50 = 0.332 mg mL-1) as compared to that of H. salicifolius (EC50 = 0.609 mg mL-1). The total polyphenol content (TPC) expressed as gallic acid equivalents (GAE) was 13.75 ± 0.50 mg GAE (g of H. salicifolius extract)-1 and 33.06 ± 0.80 mg GAE (g of H. tuberosus extract)-1. There was a correlation between the antioxidant potential of both extracts and TPC but not between antistaphylococcal activity and TPC. The obtained data suggest potential application of these extracts as the natural preparations with the biocidal activity, including those with antistaphylococcal activity. Besides, both extracts may be regarded as potential natural conservants in cosmetics as well as natural preservatives in food.

Quebec is the third-largest wine grape producing province in Canada, and the industry is constantly expanding. Traditionally, 90% of the grapevine cultivars grown in Quebec were rustic or semi-rustic and largely dominated by winter hardy interspecific hybrid Vitis sp. cultivars. Over the years, the winter protection techniques adopted by growers and climate changes have offered an opportunity to establish V. vinifera L. cultivars (e.g. Pinot Noir). We characterized the virome of leafroll-infected interspecific hybrid cultivar and compared it to the virome of V. vinifera cultivar to support and facilitate the transition of the industry. A dsRNA sequencing method was used to sequence symptomatic and asymptomatic grapevine leaves of different cultivars. The results suggested a complex virome in terms of composition, abundance, richness, and phylogenetic diversity. Three viruses, grapevine Rupestris stem pitting-associated virus, grapevine leafroll-associated virus (GLR) 3 and 2 and hop stunt viroid (HSVd) largely dominated the virome. However, their presence and abundance varied among grapevine cultivars. The symptomless grapevine cultivar Vidal was frequently infected by multiple virus and viroid species and different strains of the same virus, including GLR virus 3 and 2. Our data shows that viruses and viroids associated with the highest number of grapevines expressing symptoms included HSVd, GLR3 and GLR2, in gradient order. However, co-occurrence analysis revealed that the presence of GLR species was randomly associated with the development of virus-like symptoms. These findings and their implication for grapevine leafroll disease management were discussed.

This study reports on empirical findings of participatory design workshops for the development of a supportive user experience design system in the automotive. Identifying and addressing this area with traditional research methods is problematic due to the different UX design perspectives that might be conflicting and the related automotive domain limitations. To help resolve this problem, we conducted research with 12 User Experience (UX) designers through individual participatory prototyping activities to gain insights on their explicit, observable, tacit and latent needs. These activities allowed us to explore their motivation to use different technologies; the system's architecture; detailed features of interactivity and describe user needs including Efficiency, Effectiveness, Engagement, Naturalness, Ease of Use, Information retrieval, Self-Image awareness, Politeness, and Flexibility. Our analysis led us to design implications that translate participants' needs into UX design goals, informing practitioners on how to develop relevant systems further.

Proper species identification from ancient DNA samples is a difficult task that sheds light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies [1](Santiago-Rodriguez et al. 2016) has called into question the identification of Leishmania in South America. Here, the metagenomic data filed in MG-RAST (Metagenomics RAST server) were used for a further attempt to identify members of the Trypanosomatidae family infecting these ancient remains. For this purpose, we used two metagenomic analysis tools. In the first step, data were analysed using the ultrafast metagenomic sequence classifier, based on exact alignment of k-mers (Kraken). In the second step, we used Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. We then compared the output results. These approaches highlight some interesting findings on potential infections by human pathogenic trypanosomatids in these three pre-Columbian mummies.

Mass transfer restrictions of scaffolds are currently hindering the development of three-dimensional (3D), clinically viable, and tissue engineered constructs. For this situation, a 3D poly(lactide-co-glycolide)/hydroxyapatite porous scaffold, which was much favorable for transfer of nutrients to and waste products from the cells in the pores, was developed in this study. The 3D scaffold had an innovative structure, including macropores with diameters of 300−450 μm for cell ingrowth and microchannels with diameters of 2−4 μm for nutrition and waste exchange. The mechanical strength in wet state was strong enough to offer the structural support. The typical structure was more beneficial for the attachment, proliferation, and differentiation of rabbit bone marrow mesenchymal stem cells (rBMSCs). The alkaline phosphatase (ALP) activity and calcium (Ca) deposition were evaluated on the differentiation of rBMSCs, and the results indicated that the microchannel structure was very favorable for differentiating rBMSCs into maturing osteoblasts. For repairing rabbit radius defects in vivo, there was rapid healing in the defects treated with the 3D porous scaffold with microchannels, where the bridging by a large bony callus was observed at 12 weeks post-surgery. Based on the results, the 3D porous scaffold with microchannels was a promising candidate for bone defect repair.

The behavior of low concentrations of methanol (0.5 and 1.0 wt%) as a promoter for hydrate formation in seawater or pure water metastable solutions of methane was investigated using the classical molecular dynamics method at moderate temperature and pressure. The influence of methanol on the dynamics of the re-arrangement of the hydrogen bond network in seawater and pure water solutions of methane was studied by calculating the order parameters of the tetrahedral environment and intermolecular torsion angles for water molecules, as well as by calculating the number of hydrogen bonds, hydrate and hydrate-like cavities. It was found that hydrate nucleation can be considered a collective process in which the rate of hydrate growth is faster in systems with low concentrations of methanol and confident hydrate growth begins earlier in a metastable solution without sea salt with a small amount of methanol than in systems without methanol.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

Modern automotive systems should must satisfy strict reliability requirements. Most of the real vehicle systems and safety-critical networks have complex interconnections. Sensitivities and probabilistic uncertainties of reliability of System with Complex Interconnections (SwCI) can be investigated by the Monte-Carlo Simulation (MCS). The paper focuses on sensitivities and parametrical uncertainties of SwCIs reliability. The proposed method can be used forimplemented in the investigation of uncertainties of SwCIs reliability – in other wordsi.e., for in the determination of critical system elements and to estimateion of the Required Number of Spare Parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

In this study, Fluent software 2020R2 is used to simulate vertical axis turbine in two dimensions, aiming to analyze the influence of turbulence intensity on wake characteristics. The detailed flow field data are obtained by simulating the operation of turbine under different turbulence intensities. On this basis, the influence factor of turbulence intensity are creatively introduced into the wake mathematical formula of vertical axis turbine established by Lam, and some parameter calculations of the formula are supplemented and perfected. The results show that the theoretical model can well reflect the influence of turbulence intensity on the wake distribution of vertical axis turbines. The formula can provide a theoretical basis for the design and optimization of vertical axis turbines and the array arrangement, and can be used to predict the wake characteristics under different turbulence intensity operating conditions.

This project aims to simulate and analyze population dynamics in evolutionary systems, using stochastic differential equations and probabilistic rules for crossbreeding and benefits. The study employs advanced computational techniques to model the evolution of populations over an extended period. Specifically, we explore a scenario with ten distinct groups, each starting with an equal population and evolving under defined genetic and cooperative behaviors. The simulation spans 1000 years, incorporating individual lifespans, stochastic updates, and probabilistic interactions. To ensure robustness and reliability, the study includes detailed statistical analysis, encompassing descriptive statistics, confidence intervals, and ANOVA tests. We further visualize the temporal behavior of populations and subpopulations, providing insights into the evolutionary dynamics and stability of the system. The results demonstrate the effectiveness of balanced cooperation and genetic diversity in maintaining stable population distributions. The project showcases the integration of mathematical modeling, statistical analysis, and computational simulations, contributing valuable insights into evolutionary biology and complex systems. It highlights the importance of interdisciplinary approaches in understanding and predicting population behaviors, with implications for both theoretical research and practical applications in fields such as ecology, genetics, probability and social sciences.

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We were examining the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modelling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel Intelligent Traffic Light Controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and lever-aging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the de-sign and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results are presented, demonstrating effectiveness of the ITLC system in improving traffic flow and re-ducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated accuracy and reliability of our controller.

Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell’s response to environmental stress. Experiments have proven that the amounts of free σB (SigB) are controlled by a system of anti- (RsbW) and anti-anti-sigma (RsbV) factors expressed from the same operon as SigB. Moreover, the phosphorylation state of RsbV is controlled by phosphatases RsbP and RsbU, which directly dephosphorylates RsbV. A set of chemical equations describing the network controlling the levels of free SigB was converted to a set of differential equations quantifying the dynamics of the network. The solution of these equations allowed the simulation of the kinetic behavior of the network and its components under real conditions. In this study, the time series of protein expression measured by mass spectrometry were utilized to investigate the role of the phosphatases RsbU/RsbP in transmitting the environmental signal. Additionally, the influence of kinetic constants and the amounts of other network components on the functioning of the network was investigated. A comparison with the same simulation performed using transcriptomic dataset showed that while the time series between the proteomic and transcriptomic datasets) are not correlated, the results are the same. This indicates that when modeling is done within one dataset, it does not matter whether the data come from the mRNA or protein level. In summary, the computational results based on experimental data provide quantitative insight into the functioning of the SigB-dependent circuit and offer a template for the quantitative study of similar systems.

Multiple imputation by chained equations (MICE) is a popular and well-researched statistical approach to account for missing data in empirical research. However, applying the technique requires researchers to set dozens of smaller and larger parameters to build an adequate imputation model before estimating the analytical model of interest. It is somewhat unclear how severely small and potentially arbitrary decisions influence the quality of the produced findings. The current study tests this empirically using a simulation approach with datasets with low and high shares of missing data points that are missing at random (MAR). For each of the two specifications, 4,200 simulations are conducted where multiple parameters are randomly varied to create a diverse range of potential imputation models. The results demonstrate that even random imputation models can significantly reduce bias compared to listwise deletion since more than 90% of all simulations give less biased results. All minor decisions taken together explain between 11% and 21% of the variation in the statistics of interest. The most substantial influence on this variation is due to the selection of the imputation algorithm within MICE. These findings demonstrate that almost any imputation model, even those built with little care, will often result in a higher quality than applying listwise deletion, as long as critical assumptions, such as MAR and the avoidance of severe misspecifications, hold.

This work investigated the influence of current density, plating solution flow rate, and nozzle outlet-cathode distance on the properties of Ni-diamond composite coatings. A multi-physics field simulation is employed to analyze the interplay between current density, plating solution flow rate, and nozzle outlet-cathode distance on the flow field and electric field distribution. Additionally, particle tracing simulations were incorporated into the model to evaluate the incorporation efficiency of diamond particles during composite electrodeposition. Experimental validation of the findings shows a significant increase in the diamond particle content of the coating. This combined approach provides valuable insights for optimizing the jet electrodeposition process for Ni-diamond composite coatings with superior properties.

This work simulates the performance of lithium batteries for electric vehicles under different charge and discharge rates. The simulation is based on similarity factors for power, voltage and current, reproducing the real operation conditions of an electric vehicle at the model scale. Most current driving modes have been analysed corresponding to discharge rates from 0.1C to 0.37C. The simulation has also been applied to determine charging time using charge power in real conditions, from 6.1 kW to 18.3 kW (0.1 C to 0.3C). Driving conditions are obtained using equations for vehicle motion including all forces. Tests have been run under two configurations, continuous and alternate current circuits, to reproduce the two types of engines that electric vehicles use. The simulation shows very good agreement in charge and discharge processes, average deviation of 3% related to real conditions, and 1.6% between them, which proves the validity of the simulation process.

Construction using earth materials demonstrates ecological sustainability using locally sourced natural materials and environmentally friendly demolition methods. In this study, the environmental impact of adding cement to soil materials for rammed-earth farmhouse construction in rural China was investigated and comparatively simulated using the One Click LCA database, focusing on the conflict between sustainability objectives and the practical aspects of cement addition. By analyzing how the addition of cement aligns with local construction practices and by addressing the debate surrounding the inclusion of cement in rammed earth construction, our objective is to provide insights into achieving a balance between the environmental impact and the pragmatic considerations of using cement in earthen building practices. Three local structure scenarios are evaluated via simulations: a cement-stabilized rammed earth wall, a fired brick wall, and a localized reinforced concrete frame structure. The quantitative environmental impacts are assessed, and the qualitative differences in adaptation, economic sustainability, and other factors are examined in the context of present-day development in rural China. The results show that the use of cement-stabilized rammed earth wall-supported structures is associated with higher embodied carbon emissions compared to structures supported by reinforced concrete frames and enclosed by brick walls; however, these emissions are lower than those for brick wall-supported structures, while effectively meeting the structural requirements. In addition, the use of cement-stabilized earth for perimeter walls simplifies material management and disposal throughout the building's life cycle, and the cost-effectiveness of cement has been found to be substantially greater than that of reinforced concrete frames and brick structures, improving economic viability and social acceptability, especially among low-income communities in rural areas.

The aim of this study is to develop an African Neutral Network (ANN) model for predicting temperature and relative humidity in the greenhouse during the calendar year. Input data are ambient temperature and relative humidity outside of the greenhouse chosen for a typical meteorological year for the location of the object and the values of temperature and relative humidity of the air in the greenhouse, obtained as a result of a TRNSYS dynamic simulation of the thermal load of the facility. The goal was to show that our ANN model is capable of making predictions over extended time period. The TRNSYS model was created on a real object, taking into account all relevant parameters, in order to describe the dynamic behavior of the object as good as possible. The ANN model is formed of 3 layers, where the number of neurons in the hidden layer is obtained by training different types in order to reach the optimal number of neurons, and the values of the learning rate and epochs are also varied. The activation functions were respectively the hyperbolic tangent in the hidden layer and the linear function in the output layer. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient were chosen as the statistical criteria for measuring of the network performance. MAE and RMSE showed that the model with 6 neurons in the hidden layer gave the best results of data prediction in relation to the input data. Statistical analysis of output shows that, the RMSE and Mean Absolute Error (MAE) between the measured and predicted temperature was 0.3166 °C and 0.1002°, and the relative humidity RMSE and MAE was 5.9% and 3.4%, respectively, which can satisfy the demand of greenhouse climate control. The results of the study showed that the model can be used with satisfactory accuracy to predict the parameters of the internal environment, in relation to the complex simulation of the thermal load of the object.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

In the literature, estimation of weighted extropy are least considered. In this paper some non parametric estimators of weighted extropy are given. The validation and comparison of the estimators are implemented with the help of simulation study and data illustration. The usefulness of the estimators are demonstrated using real data sets.

The Hollow fiber membrane contactor (HFMC) is an intensification process that combines membrane and absorption technologies, being a technology in expansion, so the dynamics of this process is crucial for an increase in industrial scale-up. In this manuscript a rigorous dynamic model of an HFMC is developed and simulated in Python software for the absorption of post-combustion CO2 using a monoethanolamine (MEA) solution, which was validated using available data in literature in steady state. Dynamic simulations were performed to analyze the model's capability to predict the effect of disturbances on the responses of CO2 capture ratio and loading. Four distinct experiments were conducted by step changes and pulse disturbances at the following variables: gas and liquid volumetric flow rates, CO2 composition in flue gases, and CO2 lean load. The gas phase showed a faster response compared to the liquid. The CO2 capture ratio and rich loading presented opposite responses when disturbing both liquid flows, and for the composition and lean loading variations, the capture ratio presented an inverse response characteristic, while the loading showed a delayed response. As the model proved to be capable of returning to the reference state, it can potentially be used for control studies purposes.

This research presents a comprehensive computational analysis of the two-phase flow dynamics within a three-dimensional porous transport layer (PTL) in a proton exchange membrane (PEM) electrolyzer. Employing advanced computational fluid dynamics (CFD) and a volume of fluid (VOF) approach, the study utilizes the finite-volume method to model a time-dependent, isothermal process within a laminar flow regime. Notably, the study treats oxygen as the dispersed phase and water as the continuous phase within the system. Investigations on the anode side uncovered the formation of gas bubbles on the electrode's surface subsequent to the electrochemical reaction. The simulation, spanning 5 seconds with 0.25-second intervals, highlights the critical role of the time interval between the initiation and 2 seconds in achieving pressure and velocity equilibrium within the PTL. The initial 0.75 seconds witnessed a peak in oxygen concentration, followed by its movement across the PTL, exhibiting a transition from regions of lower to higher concentration, consistent with known physical behaviour. Remarkably, the model recorded the highest pressure of 3.5 Pa at 0.25 s. Furthermore, the study observed an incremental pressure drop from 0.25 s to 0.5 s and 1 s, approximately amounting to 28.5% and 20%, respectively, ultimately leading to a uniform pressure distribution within the model. Over time, the pressure drop intensified across the entire model due to the evolving nature of the oxygen gas. These findings provide valuable insights into the complex dynamics of two-phase flow processes within PEM electrolyzers, contributing to the advancement of sustainable energy technologies.

In this study, the generality and prediction accuracy of a generalised series model for large eddy simulation of premixed and non-premixed turbulent combustion is explored. The model is based on Taylor series expansion of the chemical source term in scalar space and implemented into OpenFOAM. The mathematical model does not depend on the combustion regimes, and has the correct limiting behaviour. The numerical error sources are outlined and analysed. The model is first applied to a piloted methane/air non-premixed jet flame (Sandia Flame D). The statistical (time-averaged and RMS) results agree well with experimental measurements, particularly with regard to the mixture fraction, velocity, temperature, and concentrations of major species CH4, CO2, H2O, and O2. The concentrations of intermediates CO and H2 are over-predicted, due to the limitations of the reduced reaction mechanism employed. Then, a Bunsen piloted flame is simulated. Most of the statistical properties of both reactive species and progress variable are well reproduced. The only major discrepancy evident is in the temperature, which is attributed to the experimental uncertainties of temperature fields in the pilot stream. These findings have demonstrated the model`s generality for both premixed and non-premixed combustion simulation, as well as the accuracy of prediction of reactive species distribution.

Embracing innovative digital technologies within educational systems is essential for overcoming challenges in modern learning environments, especially in the post-COVID-19 era. Teaching sustainability management in higher education institutions with innovative digital tools plays a fundamental role in the transition toward sustainable societies. Suitable game design elements play a significant role in facilitating sustainable learning. This study explored the effectiveness of incorporating business simulation games with project-based learning (PBL) in a flipped classroom setting. This approach was adopted within the context of a university cross-border e-commerce course to prepare students for acquiring 21st -century skills as higher-order thinking skills in a rapidly changing educational landscape. A quasi-experimental method was employed, involving a total of 60 university students from China's Zhejiang Province. Participants completed an online questionnaire designed to assess their learning engagement across three dimensions (cognitive, emotional, and behavioral) as well as their higher-order thinking skills (problem-solving, critical thinking, and creativity). The results show that the business simulation games combined with flipped classroom learning had a significantly positive impact on students’ learning outcomes, enhancing their problem-solving, critical thinking, and creative capabilities. Importantly, this approach also improved student engagement and promoted sustainable practices by applying real-life scenarios in an interactive environment. We conclude that business simulation games integrated with project-based learning (PBL) in flipped classroom settings represent a valuable educational approach. This approach not only enhances learning engagement but also fosters the development of higher-order thinking skills, encouraging students to adopt sustainable learning practices.

In this study, we introduce the dynamics of a Hepatitis B virus (HBV) model with the class of asymptomatic carriers and conduct a comprehensive analysis to explore its theoretical aspects and examine the crossover effect within the HBV model. To investigate the crossover behavior of the operators, we divide the study interval into two subintervals. In the first interval, the classical derivative is employed to study the qualitative properties of the proposed system, while in the second interval, we utilize the ABC fractional differential operator. Consequently, the study is initiated using the piecewise Atangana-Baleanu derivative framework for the systems. The HBV model is then analyzed to determine the existence, Hyers-Ulam (HU) stability, and disease-free equilibrium point of the model. Moreover, we showcase the application of an Adams-type predictor-corrector (PC) technique for Atangana-Baleanu derivatives and an extended Adams-Bashforth-Moulton (ABM) method for Caputo derivatives through numerical results. Subsequently, we employ computational methods to numerically solve the models and visually present the obtained outcomes using different fractional-order values. This network is designed to provide more precise information for disease modeling, considering that communities often interact with one another, and the rate of disease spread is influenced by this factor.

Biomass as a whole offers a more diverse potential for valorisation than any other renew-able energy source. As one of the stages in the separation of bio-oil involves the liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method has been developed for their separation with a high purity level through an approach combining liquid-liquid extraction and rectification. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software has been used. Sensitive analyses have been carried out to investigate the influence of the different pa-rameters in the rectification columns such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process as well as the possibility of regenerating the extractant has also been studied. Tri-n-octylamine has accordingly been selected as a separating agent which has been fully recycled. The end result for separating an initial 48/52 wt% ace-tol/acetic-acid liquid mixture has been acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

Thermal analysis of a hypereutectic Al-Si alloy used for an automotive part was carried out in this research work. Solidification characteristics are recognized from the Temperature-time curve and its corresponding derivatives. This analysis was successfully used in a simulation software program with the aim of improving the resultant simulation accuracy. In this study, different types of molds, with and without cooling systems, were designed and used. The melt present in the furnaces of the factory was used to simulate the casting process. The effect of different cooling rates (1.2ºC/s, 2ºC/s, 2.3ºC/s and 2.9ºC/s) on the solidification parameters have been investigated. Differences in the resultant microstructure between wall and center of the molds have been analyzed and have been related to the T-t curve. Finally, the experimental results clearly indicate that the water-cooling rate of the molds and the melt temperature directly affects the solidification process as well as the final microstructure.

The oscillation of an elastically mounted rigid cylinder excited by steady fluid flow is investigated. Regarding the nonlinearity of real practical structures like marine risers and the stay cable of a long-span bridge, the dynamic behavior of the circular cylinder is described by double Duffing equations and the aerodynamic force performance of the wake flow is expressed by wake oscillator equation. Unlike previous studies, in the present investigation, attention is focused on coupling the wake oscillator equations, taking into account quadratic terms. Following this approach, the cylinder's combined cross-flow and in-line Vortex-Induced Vibrations (VIV) are modeled more accurately. Empirical coefficients are adjusted through comparison with previous credible experimental studies and the effects of changing coefficients of the quadratic terms on major VIV parameters are investigated. The oscillating amplitude calculated by the present model is close to that of the experiment. The relative error of results obtained by the present coupled model is lower than the previous model. Moreover, the present model successfully predicts the moving trajectories of a circular cylinder under VIV in a figure-of-eight shape.